Tuning Photoinduced Electron Transfer Efficiency of Fluorogenic BODIPY‐<i>α</i>‐Tocopherol Analogues
Bibliographic record
Abstract
Fluorogenic analogues of α-tocopherol developed by our group have been instrumental in monitoring reactive oxygen species (ROS) within lipid membranes. Prepared as two-segment trap-reporter (chromanol-BODIPY) probes, photoinduced electron transfer (PeT) was utilized to provide these probes with an off/on switch mechanism warranting the necessary sensitivity. Herein, we rationalize within the context of Marcus theory of electron transfer how substituents on the BODIPY core and linker length joining the trap and reporter segments, tune PeT efficiency. DFT and electrochemical studies were used to estimate the thermodynamic driving force of PeT in our constructs. By tuning the redox potential over a 400 mV range, we observed over an order of magnitude increase in PeT efficiency. Increasing the linker length between the chromanol and BODIPY by 2.8 angstroms, in turn, decreased PeT efficiency 2.7-fold. Our results illustrate how substituent and linker choice enable "darkening" the off state of fluorogenic probes based on BODIPY fluorophores, by favoring PeT over radiative emission from the singlet excited state manifold. Ultimately, our work brings light to the sensitivity ceiling one may achieve in developing fluorogenic antioxidant analogues of α-tocopherol. The work provides general guidelines applicable to those developing fluorogenic probes based on PeT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".